B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core
<p dir="ltr">In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have b...
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| مؤلفون آخرون: | , , , , , |
| منشور في: |
2021
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| _version_ | 1864513505696153600 |
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| author | Yahuza Bello (19645930) |
| author2 | Alaa Awad Abdellatif (17151163) Mhd Saria Allahham (19645933) Ahmed Refaey Hussein (19645936) Aiman Erbad (14150589) Amr Mohamed (3508121) Mohsen Guizani (12580291) |
| author2_role | author author author author author author |
| author_facet | Yahuza Bello (19645930) Alaa Awad Abdellatif (17151163) Mhd Saria Allahham (19645933) Ahmed Refaey Hussein (19645936) Aiman Erbad (14150589) Amr Mohamed (3508121) Mohsen Guizani (12580291) |
| author_role | author |
| dc.creator.none.fl_str_mv | Yahuza Bello (19645930) Alaa Awad Abdellatif (17151163) Mhd Saria Allahham (19645933) Ahmed Refaey Hussein (19645936) Aiman Erbad (14150589) Amr Mohamed (3508121) Mohsen Guizani (12580291) |
| dc.date.none.fl_str_mv | 2021-11-08T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3126048 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/B5G_Predictive_Container_Auto-Scaling_for_Cellular_Evolved_Packet_Core/26983876 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Communications engineering Information and computing sciences Distributed computing and systems software Cloud computing Task analysis Scalability Virtualization Servers Containers Virtual machining Evolved packet core amazon web services scaling auto scaling group optimization implementation |
| dc.title.none.fl_str_mv | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks’ elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3126048" target="_blank">https://dx.doi.org/10.1109/access.2021.3126048</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_3c2c961cd9b3aec100f21fdb47650c92 |
| identifier_str_mv | 10.1109/access.2021.3126048 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26983876 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet CoreYahuza Bello (19645930)Alaa Awad Abdellatif (17151163)Mhd Saria Allahham (19645933)Ahmed Refaey Hussein (19645936)Aiman Erbad (14150589)Amr Mohamed (3508121)Mohsen Guizani (12580291)EngineeringCommunications engineeringInformation and computing sciencesDistributed computing and systems softwareCloud computingTask analysisScalabilityVirtualizationServersContainersVirtual machiningEvolved packet coreamazon web servicesscalingauto scaling groupoptimizationimplementation<p dir="ltr">In order to maintain a satisfactory performance in the midst of rapid growth of mobile traffic, the mobile network infrastructure needs to be scaled. Thus there has been significant interest in scalability of mobile core networks and a variety of scaling solutions have been proposed that rely on horizontal scaling or vertical scaling. These solutions handle the scaling of the mobile core networks’ elements on virtual machines (which normally take at while to create) with the help of customized modules at the cost of increased overheads. Utilizing Amazon Web Services (AWS) embedded features, we present two predictive horizontal auto-scalers for containerized and non-containerized versions of EPC that scales the two versions of the EPC according to their respective CPU utilization. Additionally, we propose an efficient task assignment scheme for AWS that aims to maximize throughput and achieve fairness among competing instances. In particular, we propose two solutions: Relaxed Optimized Solution (ROS) and a Heuristic Approach (HA). Leveraging AWS environment, we implemented and evaluated the two proposed auto-scaling models based on the attachment success rate, latency, CPU usage and RAM usage. Our findings show the superiority of container-based model over VM-based model in terms of resource utilization. The obtained results for the two proposed task assignment solutions demonstrates a significant improvement both in fairness and throughput compared to other existing solutions.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" rel="noreferrer" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3126048" target="_blank">https://dx.doi.org/10.1109/access.2021.3126048</a></p>2021-11-08T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3126048https://figshare.com/articles/journal_contribution/B5G_Predictive_Container_Auto-Scaling_for_Cellular_Evolved_Packet_Core/26983876CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269838762021-11-08T09:00:00Z |
| spellingShingle | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core Yahuza Bello (19645930) Engineering Communications engineering Information and computing sciences Distributed computing and systems software Cloud computing Task analysis Scalability Virtualization Servers Containers Virtual machining Evolved packet core amazon web services scaling auto scaling group optimization implementation |
| status_str | publishedVersion |
| title | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| title_full | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| title_fullStr | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| title_full_unstemmed | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| title_short | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| title_sort | B5G: Predictive Container Auto-Scaling for Cellular Evolved Packet Core |
| topic | Engineering Communications engineering Information and computing sciences Distributed computing and systems software Cloud computing Task analysis Scalability Virtualization Servers Containers Virtual machining Evolved packet core amazon web services scaling auto scaling group optimization implementation |